ReviewNature reviews. Genetics2023
Gene regulatory network inference in the era of single-cell multi-omics.
Review in Nature reviews. Genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 269 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
269 citing papers in PubMed, 1 synthesis or guideline pooled it, 401 citations in OpenAlex.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- scYeast: a biological-knowledge-guided foundation model on yeast single-cell transcriptomics.Synthetic and systems biotechnology · 2027Article
- Inferring Gene Regulatory Networks in Stem Cells: Methods and Applications.Methods in molecular biology (Clifton, N.J.) · 2027Review
- Computational approaches for multimodal lineage tracing.Nature reviews. Genetics · 2026Review
- DUET-seq: An Open-Source Droplet Platform for High-Fidelity Joint Chromatin and Transcriptome Profiling Reveals Temporal Regulatory Decoupling in Single Cells.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- GLM-Prior: a genomic language model for transferable sequence-derived priors in gene regulatory network inference.Nature communications · 2026Article
- Pan-genomics and multi-omics for deciphering genetic variation and accelerating genetic improvement in ruminant livestock.Functional & integrative genomics · 2026Review
- A transcription factor regulatory atlas for activity inference and perturbation prediction.Nucleic acids research · 2026Article
- A hybrid machine learning framework with two-step feature selection for identifying key biomarkers and drug targets in monkeypox.Biochemistry and biophysics reports · 2026Article
- Mapping ovarian cellular and molecular landscape across the lifespan of women: a scoping review.Human reproduction update · 2026Article
- Benchmarking methods for inferring single-cell transcription factor activity using large-scale perturbation sequencing data.Briefings in bioinformatics · 2026Article
- AstroLogics: a simulation-based framework for the analysis of boolean model ensembles.Bioinformatics (Oxford, England) · 2026Article
- Deciphering global transcriptional dynamics coordinated by gene-gene regulatory interactions using single-cell data.Molecular systems biology · 2026Article
- Causal graph neural networks for healthcare.Nature biomedical engineering · 2026Review
- Single-cell ultra-high-throughput multiplexed chromatin accessibility and gene expression sequencing (SUM-seq).Nature protocols · 2026Review
- Decoding Spatial Heterogeneity and Multi-Omics Regulation with Hierarchical Graph Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Single cell transcriptomic analysis by deep learning identifies novel microglial transcription regulations in Alzheimer's Disease.AI in neuroscience · 2026Article
- Single-cell multimodal profiling of pan-cancer cell lines uncovers gene regulatory principles underlying intrinsic cell states and environmental features.Nature communications · 2026Article
- Human genetics across levels of biological organization.Nature reviews. Genetics · 2026Review
- Regulatory and metabolic control of microbial biosynthetic gene clusters.Communications biology · 2026Review
209 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The interplay between chromatin, transcription factors and genes generates complex regulatory circuits that can be represented as gene regulatory networks (GRNs). The study of GRNs is useful to understand how cellular identity is established, maintained and disrupted in disease. GRNs can be inferred from experimental data - historically, bulk omics data - and/or from the literature. The advent of single-cell multi-omics technologies has led to the development of novel computational methods that leverage genomic, transcriptomic and chromatin accessibility information to infer GRNs at an unprecedented resolution. Here, we review the key principles of inferring GRNs that encompass transcription factor-gene interactions from transcriptomics and chromatin accessibility data. We focus on the comparison and classification of methods that use single-cell multimodal data. We highlight challenges in GRN inference, in particular with respect to benchmarking, and potential further developments using additional data modalities.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.